Heterogeneous spectrum sensing: challenges and methodologies

Liu, Wei; Chwalisz, Mikołaj; Fortuna, Carolina; Poorter, Eli De; Hauer, Jan-Hinrich; Pareit, Daan; Hollevoet, Lieven; Moerman, Ingrid

doi:10.1186/s13638-015-0291-8

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…The first requirement for a cognitive spectrum monitoring framework is to have an infrastructure that will support scalable spectrum data collection, transfer and storage. In order to obtain a detailed overview of the spectrum use, the end-devices will be required to perform distributive spectrum sensing [31] over a wide frequency range and cover the area of interest. In order to limit the data overhead caused by huge amounts of I and Q samples that are generated by monitoring devices, the predictive models can be pushed to the end devices itself.…”

Section: A Scalable Spectrum Monitoringmentioning

confidence: 99%

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

et al. 2018

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This paper presents end-to-end learning from spectrum data -an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

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Section: A Scalable Spectrum Monitoringmentioning

confidence: 99%

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

et al. 2018

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“…There are two types of CR-IoT networks [15][16][17][18]: (i) homogeneous CR-IoT networks and (ii) heterogeneous CR-IoT networks. In homogeneous CR-IoT networks, all CR-IoT users have the same node capabilities, including equal antennae numbers, sampling rate, and a similar signal-to-noise ratio (SNR), which may be very minimal for detection purposes.…”

Section: Motivationmentioning

confidence: 99%

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Miah

Schukat

Barrett

2021

J Wireless Com Network

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Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by $$13\%$$ 13 % over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

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“…At the moment however, heterogeneous spectrum sensing still has its own challenges according to [64]. (i) As the spectrum sensing is done by different types of devices, the data of each of the devices could be stored in different types of formats (text, binary, .…”

Section: Heterogeneous Spectrum Monitoringmentioning

confidence: 99%

“…Finally, (iv) as the different devices generate a large amount of data, there is a need for an efficient processing method to handle this data. Liu et al [64] proposes methodologies to cope with some of these challenges.…”

Section: Heterogeneous Spectrum Monitoringmentioning

confidence: 99%

Impact of EU duty cycle and transmission power limitations for sub-GHz LPWAN SRDs: an overview and future challenges

Saelens

Hoebeke

Shahid

et al. 2019

J Wireless Com Network

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Long-range sub-GHz technologies such as LoRaWAN, SigFox, IEEE 802.15.4, and DASH7 are increasingly popular for academic research and daily life applications. However, especially in the European Union (EU), the use of their corresponding frequency bands are tightly regulated, since they must confirm to the short-range device (SRD) regulations. Regulations and standards for SRDs exist on various levels, from global to national, but are often a source of confusion. Not only are multiple institutes responsible for drafting legislation and regulations, depending on the type of document can these rules be informational or mandatory. Regulations also vary from region to region; for example, regulations in the United States of America (USA) rely on electrical field strength and harmonic strength, while EU regulations are based on duty cycle and maximum transmission power. A common misconception is the presence of a common 1% duty cycle, while in fact the duty cycle is frequency band-specific and can be loosened under certain circumstances. This paper clarifies the various regulations for the European region, the parties involved in drafting and enforcing regulation, and the impact on recent technologies such as SigFox, LoRaWAN, and DASH7. Furthermore, an overview is given of potential mitigation approaches to cope with the duty cycle constraints, as well as future research directions. more and more relevant. For example, LoRa duty cycle limitations already impacts, among others, the throughput of the downlink communication, the (un)availability of acknowledgements, the feasibility of over the air firmware upgrades, geolocation inaccuracies, and scalability [1][2][3][4]. Several models predict that the probability of duty cycle violations during downlink communication will further increase, up to 20% for SigFox and 15% for LoRaWAN [5]. Similar impacts are expected for other technologies operating in sub-GHz radio frequency bands.However, despite the large impact of these regulations, many researchers are unaware of the exact limits and are not aware of mitigation techniques they can apply. Various institutes have each implemented regulations on the availability of radio spectrum for SRDs and their usage restrictions. This fragmentation causes confusion and misconceptions for researchers and manufacturers alike.

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Heterogeneous spectrum sensing: challenges and methodologies

Cited by 5 publications

References 27 publications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Impact of EU duty cycle and transmission power limitations for sub-GHz LPWAN SRDs: an overview and future challenges

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